Currently in the motor control industry, engineers require various characteristic electrical and mechanical parameters to design and control motors. The typical parameters necessary to characterize the motor's operation, for any aspect of motor development, are winding current (i), rotor position (θ), torque and speed (ω). The relationship among these parameters provides indicia of the performance of the motor and system.
Related art methods to obtain motor operational parameters, either for design or system development, rely on finite element analysis (FEA) and locked-rotor tests. FEA proves useful in motor design and can be used to develop control methods. However, FEA does not account for parameter distortion arising from influences external to the motor, such as digital signal processing, sampling, or non-linear effects due to noise or Pulse-Width Modulation (PWM) switching. All of these influences are happenstance in typical motor drive systems.
Aside from FEA, the most prevalent method for obtaining motor operational parameters employs the locked-rotor test. According to this method, the motor rotor is locked at a specific position and a voltage is induced across the motor windings. Current and voltage measurements are made for numerous rotor positions, and other parameters, such as inductance and flux-linkage, are derived from the current and voltage measurements. No converter or controller is required to perform these measurements. Once all of the required measurements are taken, the measured and derived data can be used to control the motor. Although the locked-rotor test provides highly accurate results, the measurements are time consuming and may only be obtained through the operation of an actual motor drive.
Currently, there is a trend in the motor drive industry to eliminate rotor position sensors, which are used to provide rotor position information for the control of a motor. Position sensors increase the overall cost and decrease the overall lifetime of the system. In low-cost, high-speed applications like grinders, fans and vacuum cleaners, the presence of a rotor position sensor adds cost to the motor drive system that could be avoided by smart sensor-less based position estimation techniques. Also, for high-temperature and physical disturbance applications, the presence of a position sensor to provide the rotor position is not preferred.
Additional background information on the modeling, analysis, and control of switched reluctance motors (SRMs) and sensor-less control techniques is provided in “Switched Reluctance Motor Drives” by R. Krishnan.
Some sensor-less control methods use motor parameters that are stored in the form of look-up tables within a microcontroller. “Accurate Position Estimation in Switched Reluctance Motor With Prompt Starting”, by Debiprasad Panda, and V. Ramanarayanan (IEEE publication) provides a description of state-of-the-art control techniques for sensor-less control of SRMs.
As a substitute for position sensors and look-up tables, artificial neural networks (ANNs) are increasingly being used for inferring a rotor's position using various sensor-less techniques. So far, though, the large size of ANNs has made their implementation in practical low-cost and high-speed systems impossible.
All reference material cited herein is hereby incorporated into this disclosure by reference.